An Effective Knowledgeable Label-Aware Approach for Sentential Relation Extraction

Abstract

In recent years, sentential relation extraction has made remarkable progress with text and knowledge graphs (KGs). However, existing architectures ignore the valuable information contained in relationship labels, which KGs provide and can complement the model with additional signals and prior knowledge. To address this limitation, we propose a neural architecture that leverages knowledgeable labels to enhance sentential relation extraction. We name our proposed method knowledge label-aware sensitive relation extraction (KLA-SRE). To achieve this, we combine pre-trained static knowledge graph embeddings with learned semantic embeddings from other tokens to efficiently represent relation labels. By combining static pre-trained graph embeddings with learned word embeddings, we mitigate the inconsistency between predicted relations and given entities. Experimental results on various relation extraction benchmarks in different fields show that knowledge labels improve the F1 score by 1.6% and 1.1% on average over the baseline on standard- and minority-shot benchmarks, respectively.